OpenAI: GPT 5.6 Sol price reduction (until at least Nov 21)
developers.openai.com
developers.openai.com
10 or 15 years ago if one had asked me to envision a future where a private company invents artificial intelligence, I'd have thought for sure they'd have a massive moat, be very difficult to catch, and it would create an almost instant monopoly.
Rather, it seems that selling intelligence might end up as a race to the bottom.
Who woulda thought that just having access to enough textual inputs and outputs and a vaugely similar transformer architecture would be enough to copy-cat rather useful intelligence.
Having a dude doing translations has always been useful even if they didn't understand the subject matter. Happened all the time pre LLMs
The economy he and his ilk want to build is infinitely worse.
If investors start fleeing from senseless businesses in the AI sector, that does not mean that sensible businesses will be spared. These things follow herd mentality, and the primary drivers of the herd are greed and fear, not fundamentals or business logic.
A major correction would be a bummer but we were never entitled to these abnormal gains in the first place.
China or SpaceX seem like the 2 likely candidates in 5 years, but who knows.
If (a) demand for AI continues to increase, and (b) SpaceX can get to ~$100/kg to orbit, then they will have a ridiculously deep moat. Probably more like 10 years, though.
But as you said, who knows.
If it doesn't work out, I think China's exponential terrestrial energy deployment will eventually give them the lead, IF they can get enough chips. Another big if.
You can put AI chips in datacenters in the desert for far less than $100/kg. With lots of solar power available, the option to easily access your hardware and far less radiation issues.
The datacenter in space story really only exists to make it possible for Musk to sell X to SpaceX and make more money from the IPO. That's all. There is no engineering reason.
Cooling is probably the easiest problem to solve, easier than power. And in both cases, the problem is solved by mass to orbit. All you need for cooling is a big f-ing radiator. Solar panels are chips, and not trivial to manufacture. But a radiator is just a hunk of metal with some pipes.
That's why the cost of mass to orbit is the most important thing. You can solve almost any space problem by just throwing more mass at it.
Solar panels are 4x more efficient in space. 2x because of lack of atmosphere and another 2x because there are 24 hours of sunlight in SSO.
Is it really that hard to just say, "I don't know if it will work or not"?
Ridiculous. Have you ever seen a container ship? One engine on a container ship puts out 80 megawatts, or around 80 orbital data centers' worth of power. So if there's not enough room for solar, which would surprise me greatly, then running a generator is not going to be a problem.
As for the lack of 24-hour operation, a ~50% duty cycle is a perfectly reasonable tradeoff for not having to launch something into LEO. You can easily deploy several times as many installations for the same money if they don't have to work in space. And they will be maintainable, to boot.
And I fully sympathize with the need to remove these things from local and even national sovereignty, having seen as an increasingly-horrified American just how destructive populist influence can be.
But putting millions of servers in orbit? That just doesn't seem like the way forward, for so many reasons.
As for weather, OK, fine, whatever. Deploy 6x as many.
if you can't put them outside of Amarillo Texas without people throwing a fit then you can't put them anywhere. I mean freaking Pantex is there ffs!
Musk's bet is dysfunctional politics will make it impossible to build enough data centres and the energy needed to power them. There are many, many reasons that might be wrong. However, if the economics are even close to viable, they could start throwing up data centres quicker than anyone can build them terrestrially (at least in the democratic west).
GMAFB.
It's stock pump bullshit from a guy who has figured out how to extract the maximum from stock markets.
But yeah, I'm sure you people with Elon Derangement Syndrome actually have it all figured out /s
well, no, they announced the concept of a space-optimized Vera Rubin designed with SpaceX:
> NVIDIA and SpaceXAI are working to adapt that foundation to the requirements of orbital computing while preserving a common NVIDIA architecture and software ecosystem.
The press release is really announcing that SpaceX's terrestrial data centres are going to use Vera Rubin.
https://nvidianews.nvidia.com/news/spacexai-adopts-nvidia-ve...
But anyone who thinks they can predict those prices in ten years is wildly overconfident.
> then they will never work and SpaceX will fail
Space Datacenters eventually get quietly discontinued after finally killing the zombie brand at which point the only reaction is "huh, remember that?"
https://techcrunch.com/2026/08/21/teslas-solar-roof-is-dead-...
ETA: If you had invested $1,000 in 2016 when Tesla Roof was announced, you'd have $25,000 today. 25x return in 10 years.
He even raised money on that premise.
He is a pathological liar, so is Dario. Don’t rely on the benevolence or truthfulness of these people.
They will say whatever is beneficial to say in the moment.
Referring to a baseless prediction by Sam Altman that AI will become like electricity without any push-back? Who really thinks Sam is working toward that future?
He already worked to undo every early promise made (non-profit, open source models, strong governing board, strong ethics/alignment/security focus). He's flip-flopped on other things like first characterising Trump "an unprecedented threat to America", then contributing 1M USD to Trump's inaugural fund far exceeding his earlier political contributions. Lately OpenAI, under his supervision, has also been working with Anthropic to lobby regulators in Washington for restrictions on open weights models - why so if not to undermine a free market in favour of an oligopoly?
Beyond that, you have the simple fact that most of his personal wealth and very probably the fate of OpenAI hinges on AI inference NOT becoming an interchangeable commodity.
I mean.. Honestly. The naivete is downright astounding.
I live in Germany where people won't stop whining about electricity prices, and I pay 75€/mo.
Here is a project that guides you through it if you want to prove to yourself that it works https://github.com/arcee-ai/DistillKit
GPU kernel optimization is just the kind of well-bounded problem with clear success criteria that AI loves.
It's about evidence this is an active force in competition in LLMs.
It's also how providers build their smaller models out of their larger ones; they publicly talk about the process.
Make sure to stay updated!
If we wanted to compare model responses, we would give all models system prompts with model names, thereby fixing the Kimi misattribution.
The reason Kimi often states its name as Claude is likely because we can actually run it without the mandatory system prompt, smoothing over awkward competitor mentions.
[1] https://www.anthropic.com/news/detecting-and-preventing-dist...
I’m really not sure how you’re getting “exact” here.
They are crying about theft after committing the largest theft in human history.
I believe it can be used in the RL / fine tuning sense, in which case 150,000 requests, assuming every one was detected, could move the needle in quality.
I agree it couldn’t replace all of pre and post training , but I don’t think that’s the claim. You do typical training, then distill really difficult cases.
That said, there are other moat factors like, a US company needing to use a US AI provider, sticky customers due to corporate onboarding friction, and others. Not nothing, but not as large a moat as some imagined.
There were somewhat good reasons to think it needed more than just this data-driven ML approach.
Then i think tool use became a priority or at lest a sibling priority to more data/more params. Along with multiple specialized models communicating with each other which is sort of a special case of tool use. That pretty much brings us to today.
Personally, I came to this conclusion early this year. To acquire the data that AI Companies are using to train their models is low cost and once they have it, they can refine and store it. Creating the LLM takes a bit of money but it is not a serious blocker. Clearly, the Chinese companies can make AI so they will drive down costs. There is a need for good AI (Not just Great AI) and it is not cost prohibitive to make good AI (The same with specialized AI).
My prediction is that AI will spilt into two categories, Great AI (High Cost) and Good Enough AI (Low Cost). Which for the long run of AI and companies that use AI, this is good.
And with the sheer volume of data created from that, coupled with benign-seeming prompts like "plan out your reasoning in a document before implementing" that could never be patched without breaking existing customer workflows... there's more than enough for someone to distill on. Even if that only gets them to not-quite-frontier, if you're pushing the frontier every few months, they're only ever a few months behind you.
The primary resource you need to train LLMs is money and China has plenty of that.
Besides, identity verification that actually works at scale is a much harder problem than identity verification which is good enough to satisfy your compliance people and regulators. Especially if the fraudsters have a major world government standing behind them, and if their aim is to be identified as a real customer, not one customer in particular.
Distillation was big news a year or even 6 months ago, but as far as we can tell it's not really a moat anymore. Now that multiple players have trillion+ parameter models and the capacity to post-train them, there's no putting the genie back in the lamp.
Same problem: if humans can see the output, it can be copied.
If I run out of tokens on ChatGPT of course I will try Claude. I never ran out of Google searches so no reason to try Bing
More like the other way around - Claude burns tokens faster than any other LLM.
At some point Google gets suspicious of your persistent searches and makes you solve captchas and puts cooldowns on your searches.
ChatGPT is AI for the average non-techie the world over, but the average non-techie isn't eager to pay for it. The more progress that's made, the less incentive to pay - most people are happy with the total garbage spewed by google AI overview. They'd be happy with google's 30b MoE gemma, whose performance will likely be squeezed down to something that can run on a phone in 2-3 years. Why would they pay $20 a month?
It's why OpenAI is pushing a variety of things such as ads and offer a more polished ui/ux than the competition, I think. The models are already good enough for people who just want to know how much sugar to add to their cake or when's the next basketball match their team plays - it's OpenAI's game to lose those people, by annoying UX and whatnot. If they can make a few bucks off of every one of their non-paying users it'll stretch their runway immensely. Those users will never go to Antrophic or some cheap Chinese model, but they might defect to Google because a popup on Android / in Chrome told them to.
I had a discovery call last week with someone who did not realize he could use ChatGPT for work. It was a revelation that he could drag a PDF into ChatGPT and it could summarize it for him.
FWIW, guy in his late-30s in a pretty senior sales role.
The frontier models are a replicator that can give you another replicator which specifically produces tea, earl grey, hot, when you push the single button, and does nothing else.
The quality of the harness UX, and random fun crap like Sora, it's a shame that OpenAI killed that so soon, and also Group Chats in ChatGPT.. they risk running a Googlelike reputation at this rate
Maybe ultimately whomever can be the "Apple of AI" will win
And I'm saying this as somebody that's made millions selling AI software in the last few years...
The Chinese models are pretrained on large clusters just like OpenAI ones are. Yes, they use outputs of the frontier models to further improve the final model, but even without those outputs they'd still have very strong models.
It's not like in a world without distillation things would be much different as you claim.
Tricks like distillation save compute in the RL leg of the process - where a lot of the frontier labs puts their own training run compute.
Transformers are like just a step or two removed from being fancy convolutional neural networks. I guess I'm just surprised that it didn't turn out to require more 'special sauce' with extremely elaborate internal architectures, and less of a big-data approach.
Because the data is so central in building these LLMs, rather than some special insights or ideas in the model architecture, or very special hardware requirements, the field is much more open than I would have guessed some years ago. And it's the fact that the data is so central that makes distillation possible in the first place.
Sort of. It means the country on the verge of monopolizing all aspects of hardware production (China) doesn't need to rely on outsiders for the software. So while that weakens one monopoly it strengthens another.
At a regular store not some weird nerd laptop for normies.
The times of China needing Nvidia chips is quickly coming to an end.
I guarantee that China will scale faster, build faster, and ultimately produce far more chips than the rest of the world combined in 5 years.
Our export controls sank the west. It would have been better to allow them to use Nvidia chips. Now they will have chip fabs that aren’t quiet as good but way more of them. Their investment into sustainable will make the power so cheap that the less efficient chips will not be a relevant issue
Can you buy a phone/laptop with no parts from USA/Japan/Netherlands/Taiwan/South Korea?
China has mastered lower parts of the value chain, but the most advanced and profitable ones (chips, turbines, frontier models) are solidly dominated by the US and its allies.
That might change, but when? The demographic and economic advantages that helped China rise quickly are exhausted. The Chinese economy is struggling to unwind the property bubble and manage a huge amount of surplus production that can no longer be sold to the US. The country is shrinking in population. Urbanization, which was previously a sure fire way to achieve growth, is now at ~70%, compared to the US' 80%.
It seems unlikely that China will be able to fully supplant the rest of the world unless the government relinquishes its tight control of the economy. There's just not enough centrally-plannable growth left.
The US will be (is) forced to work with everyone not named China or Russia, to ensure it can compete with China's concentrated industrial might. That means making iPhones in India, or Mexico, or Vietnam. That means Micron building more RAM production domestically. It should mean helping ASML stay out in front of China's incoming silicon wave. And so on.
It'll be China + allies vs the US + allies. It's going to be intense and very expensive. A huge amount of global manufacturing has to be built up outside of China.
And China does indeed have enormous problems brewing, including a staggering 5x increase in homelessness in just roughly seven years (to go with its collapsed housing market):
https://www.upi.com/Top_News/World-News/2026/01/14/beijing-b...
Over the next 20-30 years robotics + AI will brutalize countries with very large populations when it comes to employment, especially those that depend on volume manufacturing heavily to absorb labor. The US for its part should be focused on rapidly shrinking its population, pushing up-skill, restricting immigration based on points (ala Canada et al.), and focusing entirely on quality of life / standard of living per capita as the population declines (let it happen rather than fighting the big trend, boost GDP per capita via robotics + AI, higher productivity).
I'd want to be a small, decently well educated, high functioning nation, with powerful military friends, and far from China. Estonia perhaps (Russia poses no serious terminal threat to them so long as NATO exists or a European alternative). Poland has some cultural mojo right now as well, they could be interesting over the next few decades.
Interesting to hear a new take. Not what you commonly hear. Most focus on the damage caused by a shrinking and aging population. What's your opinion on that situation?
I would think the trick will be the figure out how robotics + AI will enable a new level of elder care, but it's hard to imagine robots being delicate and nuanced enough to do that kind of work.
Its not that wild of an idea. It's not like e.g. chipmaking where even just knowing how things are done doesnt mean you can copy it.
They disabled the temperature and seed parameters. There's still logprobs, so they aren't as closed up as Anthropic yet.
I might write about an article of the history of LLM APIs, I used to think the ChatGPT was going to be a de facto standard like intel's 80866 mutated into x86, but it seems to be a bit more nuanced and diverse than that, vibecoding introduced so much complexity because the vibecoding product itself became vibecoded so the enshittification was accelerated, many such cases.
Your seeing the AI labs respond by never publishing chain of thought now and in the future, I see them not even publishing their top models as a general purpose API and instead using it to drive their own AI apps, which will obscure even more model output. Anthropic Mythos was internal only for many months for example.
They're not, right? If it's really easy why there are no counterparts of DeepSeek from the Europe or Japan?
GPT 5.6 Luna at $0.20 per 1M is pretty good for 80% of enteprise applications.
Probably not through what we call distillation now. There’s some magical technique hiding there, go find it!
Why shouldn't it work though? It's just models teaching other models same way humans are.
Model Input Cached input Cache writes Output
gpt-5.6-sol $4.00 $0.40 $5.00 $20.00
gpt-5.6-terra
$2.00 $0.20 $2.50 $12.00
gpt-5.6-luna
$0.20 $0.02 $0.25 $1.20
There are no open source models, at least not useful ones (yet) [0]. Open weight is not the same as open source. The current "open weight" models are just opaque binary blobs you can run on your own computer instead of through a web API.
Training data and code.
While the training code and data are the true source. Since if you want to robustly modify the LLM that's actually what you need.
But since "compilation" (training) is extremely compute intensive this isn't something accessible to anyone without an entire datacenter.
Anyway semantics aside having the binary is still infinitely better than dealing with an api as far as privacy and control go.
I don't know LLM theory well enough to say if there's some secret sauce they can hold back that makes training ineffective. Less effective I'm sure, we don't have access to their smart training schemes, but post-training should always be possible IIUC.
post-training is like writing a wrapper around the binary. It is closer to building on top of than truly modifying, in that you can tailor things to your needs slightly but cannot make fundamental changes to the underlying thing.
For a stretched analogy, I think it is more like LEGO sets. Someone hands you a 10,000 piece masterpiece, and a box of unused LEGO parts. Hackers on HN object that the LEGO part manufacturing process is not included, you can't make your own parts, etc. But it's LEGO. You can pull apart the model, see how it is constructed, add your own refinements and features, or even redo it from the ground up. In a practical sense having knowledge about the factory making the parts doesn't really matter here.
https://en.wikipedia.org/wiki/Ablation_(artificial_intellige...
Put in the work. This is akin to asking how to remove Rust from a Rust project; just because something is legally available to you doesn't mean you wont need to apply dome elbow grease, depending how deep the changes you want are, ablation, fine-tuning, or distillation are tools you can use to remove "censorship"
Open source means you reveal how you created this binary.
you need to "literally" go read the definition of open source software or even ask an LLM to define it for you. Weights + inference code are not the source code they're more like the compiled binary. Making modifications to the behavior of a model with additional training is like writing a mod for minecraft. Sure, you can change things but it doesn't make it open source.
Calling these models "open source" is an old trap that software companies use to use. Free to download but then, once you're fully comitted, the trap snaps shut and you must pay up to continue.
The actual problem is that we know nothing about the training set of any open-weights model. They could be intentionally biased to influence users, from political censorship to brand advertising, or general shaping of cultural norms. You run the model on own hardware not knowing if it is designed to act against you. Having whole chain open source would allow audit and reproducing the results.
Is it just the supplementary data/code for how they were trained, not just the final product?
I don't mind open-weight models, but they are not open source. It's like bringing home a dog from the rescue and just hoping that it doesn't have a history of biting kids in the face. You just can't know, because you don't know the full history. You can try to add new training (fine tune) to tell it not to bite kids, but that's it.
That said, I've noticed the training procedures and corpus size of more useful open/available weights models are settling down more than I expected. Wonder if crowd sourcing good training data, even if it's just expensive model coding session transcripts, has potential to level the landscape some.
I am the type of coder that vibe codes - I talk to the agent about a problem, have it write a plan, red team the plan, and then implement the plan. These projects are things which haven’t really been done before, or if they have it’s not public or not in many places.
What I find is that Sol is hyper-left brained. Super focused on small details. When given a longer task with multiple steps it might go really hard on one of the early steps and it will validate, test, make safe, so much to the detriment of progressing the task within reasonable parameters for the project.
It also starts to sound crazy when you ask it for an update. It starts naming things in weird ways and the sentences don’t really make sense. It’s as if you’ve approached an engineer who has been hammering on something and he speaks to you in the lingo of his latest function, even though when you ask him for a status you are obviously asking about the whole project.
Fable on the other hand seems to remain coherent over time. It’s as if it remains aware of the longer run task. It’s got a bit more balance between left and right brain.
So OpenAI really need to find a balance between long term goal thinking and the very small task at hand.
For coders who apply Sol on specific functions or narrow tasks I’m a certain it is great. For me, a vibe coder, I need one that will be a bit more aware of the whole thing through these longer running tasks.
Feature request for Artificial Analysis, allow us to see these live prices on the pareto. It would amazing to also see what a 25,50,75,100 % utilised subscription costs compared to raw tokens.
Company empathy does exist, just look at how easy or hard it is to reach a company when you have a problem. How do they try to solve it for you? Is it a brick wall, for example Google when you have a problem. People quite often like dealing with small businesses because they can reach a singular human and have them as an interface to the problems they face now and in the future.
Agentic loops and the models underneath them can have a simulacra of empathy too. Not every model just blindly agrees with users, and some have a much better depth in picking up context clues that the user on the other end is having a hard time. Businesses just typically aren't running more expensive and fragile systems like that though.
For example, health insurance providers are renowned for not being empathetic. Charities are the opposite. Sometimes companies even build it into their identity, e.g. Cards Against Humanity.
As for AI, I haven't seen a strong difference in empathy but it's definitely true that the big AI companies at least try to make their models moral and empathetic. Even if it mostly ends up just being annoying.
It's funny how people make these alignment comments while ignoring how misaligned the leadership at these companies are right form the get go and they just play mental gymnastics to deflect those facts when confronted with them.
I've noticed also that 5.6-Sol is more concise with output than Fable (and let's not talk about Opus, which is even more wordy).
FWIW, we use ChatGPT for our primary model and use Claude to do the reviews. This works better than ChatGPT doing it's own review even with a clean session/context.
I've done this tens of times between these two models and it works great in my experience. Sol initial back and forth with me. Commit. Let Grok review. Sol fix. Only then do I start reading the code.
Like, the quality of the anthropic models is fine, but they’re so incredibly slow. Claude reads files one at a time while codes dispatches tool calls three or four a time.
You should be doing this for every solution.
Even Fable reviewing itself will find issues, unproven assertions, etc. Same for Codex models. A review loop is critical.
I believe the reason we have not seen a Fable-level model from OpenAI yet is because doing so would box them in on costs just as harshly as it has boxed in Anthropic. They are letting Anthropic make this mistake.
On OpenAi even 20$ has Sol and all your usage can be Sol
This should not be surprising at all. Every new students spends tiny fractions of time learning knowledge that took many lifetimes to discover. This fundamental to the progress of intelligence and understanding.
It should not be surprising that AI can be distilled. It's the logical method of training; I would hope that each frontier model is in fact not trained 'from scratch' each time.
We should expect future frontier models are simply distilled versions trained by specialist models, the same way humans learn from a series of professors, papers and canonical books on each different subject material. Models like this can be trained incrementally, or a so called Mixture of Experts (MoE).
This argument is exactly why we should not anthropomorphise models.
You are comparing the way a human brain learn with training a statistical model. You can't just "this is like learning so don't be surprised".
It takes a child one minute to learn how to open a padlock. Teach that to a robot with your analogies.
Why is large better than medium to the average end user of ChatGPT though?
I don’t think there’s a way to name these things that will satisfy everyone.
My brain's initial conception of the concepts was earth-relative, so I mapped it as:
Sol = big, it's the sun Luna = medium, in-between sun and earth, space Terra = small, terrestrial
But alas
The problem becomes when you add in the adjustable reasoning efforts and you end up with {model, reasoning_effort} combinations that end up completely obviating particular model classes altogether for at least some percentage of queries; e.g. with GPT 5.6 the price/performance Pareto frontier is dominated by permutations of either Luna and Sol, with Terra nowhere to be seen (but then if you need "large model smells" that aren't captured by your benchmark you can't even rely on this, as a model like Luna simply isn't capable of encoding sufficient world knowledge in its weights to perform certain tasks at any reasoning level but you might be able to get away with Terra on low reasoning, but no one seems to be covering this for some reason).
Calling something "small" might make it sound inferior to competitors. And S/M/L gets awkward as soon as you have more than three sizes.
This naming system can get near-infinitely bigger or smaller.
Making 2/10 permanent would be a killer move and make a strong argument against open-weight. For the sake of the open weight ecosystem I hope they do not.
I'll be sure to tell the economists.
You're just price insensitive. The point of a Pareto frontier is mapping cost and utility. Not everyone can afford to run fable all day.
It really reminds me of pay-to-win games at this point: Two currencies (credits, tokens), both with a floating, intransparent exchange rate between each other and real money, random airdrops...
[1] https://help.openai.com/en/articles/12642688-using-credits-f...
$20/m = appox API $700/m
$100/m = appox API $3,500/m
$200/m = appox API $14,000/m
[0] https://www.reddit.com/media?url=https%3A%2F%2Fpreview.redd....
The people who give them the money are greedy, and hopefully in for a rude awakening. Starting from Nvidia's vendor financing which has a very direct benefit to them, through to every company and oligarch investing into data centres in the hopes of being one of the ones left capitalizing on capturing the livelihoods of the majority of what remains of the "middle class".
It's either hopium or a truly horrific dystopia. Something's going to have to give.
aside from the obvious IP theft problem, it's probably most dangerous for Chinese users outside China to use the Chinese models.
the new price is welcome but still 60% more expensive than 5.4 (which is now comparable to 5.6-terra).
i conveniently also leave out the "-pro" tiers, which was comically expensive. but i feel like openai consider them suitably comparable to sol, and it must be them offering a huge bargain.
Losing money on each subscriber?
If anything they'd keep the sub and use the API if they blow past the usage.
You add requirements and make previous tests invisible to see how pigeon brained the model is - Sol and Fable seem to rank the same as Opus tends to fall behind
Though I think they gave a banked reset this time.
I tried it again today because of the discount, it told me it couldn't run acceptance tests because a .env file did not exist, and when I showed it the damn file it went "ah, it's there now". I think it was the first time I've ever had an agent try to gaslight me.
But I continued to work with it and found that it was mostly my own style of interacting that needed to change. In a way it is similar to a new co-worker, they have their own personality and ways of working. Once I figured that out I have been able to get very good work out of Sol.
Sol seems to work better when you are clear, precise, direct and unambiguous. The model seems annoyed if things aren't spelled out. Not micro-managing, it seems to have a high bar for specific intent.
When I get Fable to write out specs for Sol, I tell Fable that Sol is a nit-picking literalist that is exceptional at instruction following. So far this description has lead Fable to generate specs that Sol implements at a high quality.
You must not have been using agents for very long then because this behavior has been around for some time now.
They are probabilistic models that are simply giving you words in a sequence they think makes the most sense to them.
No one is "lying" to you :)
they discovered a great way to destroy their own stickyness and make ppl build generic ai solutions.